Direct RNA sequence design under codon constraints using expressive tensor-based secondary structure models

TL;DR

This paper introduces a novel tensor-based algorithm for direct RNA sequence design that efficiently samples and computes thermodynamic properties based on detailed secondary structure models, advancing the field of codon optimization.

Contribution

It presents the first global sequence design method using a highly accurate free energy model with tensor-based secondary structure thermodynamics, enabling efficient sampling and computation.

Findings

Algorithms enable sampling from Boltzmann distribution based on detailed free energy models.

The methods allow exact computation of free energies, base pairing probabilities, and marginals.

Parallel CPU and GPU implementations significantly speed up computations.

Abstract

Nucleic acid sequence design via codon optimization is a fundamental task with applications across synthetic biology, mRNA therapeutics, and vaccine design. Given a target protein, it is a major open challenge to navigate the combinatorially large design space of codon sequences mapping to its amino acid sequence. Computational approaches generally seek to optimize simple objectives based on the codon sequence, possibly together with more complicated contributions based on secondary structure analysis. In this work, we demonstrate a direct and efficient algorithm to sample sequences from a suitable Boltzmann distribution defined in terms of the codon sequence and a fully detailed secondary structure free energy model, as well as related algorithms for exact computation of statistical quantities such as free energies, base pairing probabilities, and base and codon marginals. These algorithms draw upon a recently developed tensor-based formulation of secondary structure thermodynamics and demonstrate, for the first time, that global sequence design can be accomplished with respect to a highly accurate free energy model. Moreover, the algorithms can leverage any available CPU and GPU resources in parallel for massive computational speedups.